{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gas Radial flow\n",
    "\n",
    "The PVT data from a gas well in the XYZ Gas Field is\n",
    "given. \n",
    "\n",
    "The well is producing at a stabilized bottom-hole flowing pressure of\n",
    "3600 psi. The wellbore radius is 0.3 ft. The following additional data is\n",
    "available:\n",
    "\n",
    ">k = 65 md   h = 15 ft   T = 600°R\n",
    "pe = 4400 psi re = 1000 ft\n",
    "Calculate the gas flow rate in Mscf/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Gas.txt', names= ['P(psi)' , 'mu_g' ,'z'],sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 65\n",
    "h = 15 \n",
    "T = 600\n",
    "re = 1000\n",
    "rw = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P(psi)</th>\n",
       "      <th>mu_g</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>0.01286</td>\n",
       "      <td>0.937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>800</td>\n",
       "      <td>0.01390</td>\n",
       "      <td>0.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200</td>\n",
       "      <td>0.01530</td>\n",
       "      <td>0.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600</td>\n",
       "      <td>0.01680</td>\n",
       "      <td>0.794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.01840</td>\n",
       "      <td>0.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2400</td>\n",
       "      <td>0.02010</td>\n",
       "      <td>0.763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2800</td>\n",
       "      <td>0.02170</td>\n",
       "      <td>0.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3200</td>\n",
       "      <td>0.02340</td>\n",
       "      <td>0.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3600</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>0.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4000</td>\n",
       "      <td>0.02660</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4400</td>\n",
       "      <td>0.02831</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    P(psi)     mu_g      z\n",
       "0        0  0.01270  1.000\n",
       "1      400  0.01286  0.937\n",
       "2      800  0.01390  0.882\n",
       "3     1200  0.01530  0.832\n",
       "4     1600  0.01680  0.794\n",
       "5     2000  0.01840  0.770\n",
       "6     2400  0.02010  0.763\n",
       "7     2800  0.02170  0.775\n",
       "8     3200  0.02340  0.797\n",
       "9     3600  0.02500  0.827\n",
       "10    4000  0.02660  0.860\n",
       "11    4400  0.02831  0.896"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['2P/muZ'] = 2*df['P(psi)']/df['mu_g']/df['z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P(psi)</th>\n",
       "      <th>mu_g</th>\n",
       "      <th>z</th>\n",
       "      <th>2P/muZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>0.01286</td>\n",
       "      <td>0.937</td>\n",
       "      <td>66391.033227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>800</td>\n",
       "      <td>0.01390</td>\n",
       "      <td>0.882</td>\n",
       "      <td>130507.838627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200</td>\n",
       "      <td>0.01530</td>\n",
       "      <td>0.832</td>\n",
       "      <td>188536.953243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600</td>\n",
       "      <td>0.01680</td>\n",
       "      <td>0.794</td>\n",
       "      <td>239894.446444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.01840</td>\n",
       "      <td>0.770</td>\n",
       "      <td>282326.369283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2400</td>\n",
       "      <td>0.02010</td>\n",
       "      <td>0.763</td>\n",
       "      <td>312982.922869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2800</td>\n",
       "      <td>0.02170</td>\n",
       "      <td>0.775</td>\n",
       "      <td>332986.472425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3200</td>\n",
       "      <td>0.02340</td>\n",
       "      <td>0.797</td>\n",
       "      <td>343167.218951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3600</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>0.827</td>\n",
       "      <td>348246.674728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4000</td>\n",
       "      <td>0.02660</td>\n",
       "      <td>0.860</td>\n",
       "      <td>349711.488022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4400</td>\n",
       "      <td>0.02831</td>\n",
       "      <td>0.896</td>\n",
       "      <td>346924.357875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    P(psi)     mu_g      z         2P/muZ\n",
       "0        0  0.01270  1.000       0.000000\n",
       "1      400  0.01286  0.937   66391.033227\n",
       "2      800  0.01390  0.882  130507.838627\n",
       "3     1200  0.01530  0.832  188536.953243\n",
       "4     1600  0.01680  0.794  239894.446444\n",
       "5     2000  0.01840  0.770  282326.369283\n",
       "6     2400  0.02010  0.763  312982.922869\n",
       "7     2800  0.02170  0.775  332986.472425\n",
       "8     3200  0.02340  0.797  343167.218951\n",
       "9     3600  0.02500  0.827  348246.674728\n",
       "10    4000  0.02660  0.860  349711.488022\n",
       "11    4400  0.02831  0.896  346924.357875"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I1EBBWkREWq5//9ucrrFuHfzrX/W/WVBEpAYK0iIi0nIYBvzv/168WTA/3/24bhYUkQakIC0iIt4pMdEcJa5pebj58821lxMTzZsFt227GJ51s6CINBEFaRER8U7+/jU/9a/y0dpjx0JsrG4WFBGPUZAWERHvdOkjtH/9a/jVr8w5z/7+8OabF2t1s6CIeICCtIiIeK/Zs+HgQTNMVwZqMKd1fO97F28WjIzUzYIi0uQUpEVExPsYhrnOc1ISZGVd3G+xwLx5Zni+4w7dLCgiHqUgLSIi3qOiAtLSYMECc/UNMEeay8shIMC8sdAwICLCs+0UEQH8PN0AERERnE547TUzIP/0p2aIbtMGBgwwQ/S8eeBwmL/OmWPecCgi4mEakRYREc85fx5WrYLnnoMDB8x9118Pkyeb4XrRIjM8V954eOkNiDUtjSci0kQUpEVEpOmdPg1//CMsXQoFBea+G2+EhASYOBHatzfXiK4aoitVbpeXN2mTRUQupSAtIiJNp7gYUlLgpZfg22/NfeHh8NRTMGGCOZ2jUmJi7efRSLSIeAGPzpFevnw5vXv3pn379rRv357IyEjef/9913HDMEhMTMRutxMUFMSgQYP47LPP3M5RVlbG5MmTCQkJoW3btowaNYrDVZ9qBRQXFxMbG4vNZsNmsxEbG8vJqov3A4cOHWLkyJG0bduWkJAQpkyZgsPhcKvZvXs3UVFRBAUFcdNNNzFv3jwMw2jgT0VEpAU6dgyefho6d4ZnnzVD9K23wsqV8O9/m1M5qoZoEZFmwKNB+uabb2bRokV88sknfPLJJ9x///08+OCDrrC8ePFikpOTWbZsGTt37iQsLIzo6GhOnTrlOsfUqVNJS0sjNTWVrKwsTp8+zYgRIyiv8l9+48aNIy8vj/T0dNLT08nLyyM2NtZ1vLy8nOHDh3PmzBmysrJITU1l7dq1TJs2zVVTWlpKdHQ0drudnTt3kpKSwpIlS0hOTm6CT0pEpJk6dMgMyV27wuLF5pSOXr1gzRr44gt44gkIDPR0K0VE6sfwMsHBwcb//M//GBUVFUZYWJixaNEi17Hz588bNpvNWLFihWEYhnHy5EkjICDASE1NddUcOXLE8PPzM9LT0w3DMIzPP//cAIycnBxXTXZ2tgEYX375pWEYhrFx40bDz8/POHLkiKtmzZo1htVqNUpKSgzDMIxXXnnFsNlsxvnz5101CxcuNOx2u1FRUVHn91dSUmIArvM2NofDYaxbt85wOBxNcj3xLup/3+bR/t+zxzB++UvDaNXKMMwF6wyjXz/DePddw7iKvzOl/vTn37ep/69NXfOa1yx/V15eTmpqKmfOnCEyMpL9+/dTWFhITEyMq8ZqtRIVFcX27dsByM3Nxel0utXY7XYiIiJcNdnZ2dhsNvr16+eq6d+/Pzabza0mIiICu93uqhkyZAhlZWXk5ua6aqKiorBarW41R48e5UDlneYiIr7u//4Pxo6F22+Hv/wFLlyA+++HzZshOxtGjtRDVESkxfD4zYa7d+8mMjKS8+fPc91115GWlkbPnj1dIbdjx45u9R07duTgwYMAFBYWEhgYSHBwcLWawsJCV01oaGi164aGhrrVXHqd4OBgAgMD3Wq6du1a7TqVx7p161bj+ysrK6OsrMy1XVpaCoDT6cTpdNbyqTScyms0xbXE+6j/fVtT9r8lJwe/RYvw27jRta9i+HAqZszAqBzIuHCh0dshF+nPv29T/1+bun5uHg/S3bt3Jy8vj5MnT7J27VrGjx/P1q1bXcctl4xcGIZRbd+lLq2pqb4haozvbjS8XHsWLlzI3Llzq+3PyMigTRPeWJOZmdlk1xLvo/73bY3W/4bBjZ9+yq1//zs37t5t7vLz48jAgex75BFKu3aFEyegSriWpqc//75N/V8/Z8+erVOdx4N0YGAgt9xyCwB9+/Zl586dvPTSSzz99NOAOdrbqVMnV31RUZFrJDgsLAyHw0FxcbHbqHRRUREDBgxw1Rw7dqzadY8fP+52nh07drgdLy4uxul0utVUjk5XvQ5UHzWvaubMmSQkJLi2S0tLCQ8PJyYmhvbt21/uo2kQTqeTzMxMoqOjCQgIaPTriXdR//u2Ruv/igos69fj99xz+O3cCYAREIDx+OOUT59Ox1tvpfa/FaWp6M+/b1P/X5vKGQRX4vEgfSnDMCgrK6Nbt26EhYWRmZnJXXfdBYDD4WDr1q0899xzAPTp04eAgAAyMzMZM2YMAAUFBeTn57N48WIAIiMjKSkp4eOPP+buu+8GYMeOHZSUlLjCdmRkJElJSRQUFLhCe0ZGBlarlT59+rhqZs2ahcPhIPC7O8wzMjKw2+3VpnxUZbVa3eZVVwoICGjS39hNfT3xLup/39Zg/X/hArz9NixYAPn55r7WrSE+HstTT2EJD/fsUlBSI/35923q//qp62fm0SA9a9Yshg0bRnh4OKdOnSI1NZUtW7aQnp6OxWJh6tSpLFiwgFtvvZVbb72VBQsW0KZNG8aNGweAzWZjwoQJTJs2jRtuuIEOHTowffp0evXqxeDBgwHo0aMHQ4cOJT4+nj/+8Y8A/OpXv2LEiBF0794dgJiYGHr27ElsbCzPP/883377LdOnTyc+Pt41ajxu3Djmzp1LXFwcs2bNYt++fSxYsIA5c+ZccaqJiEiz5nDA3/5mPq77q6/Mfe3awaRJ8F//BTXchyIi4gs8GqSPHTtGbGwsBQUF2Gw2evfuTXp6OtHR0QD8/ve/59y5c0ycOJHi4mL69etHRkYG7dq1c53jhRdeoFWrVowZM4Zz587xwAMPsGrVKvz9/V01r7/+OlOmTHGt7jFq1CiWLVvmOu7v78+GDRuYOHEiAwcOJCgoiHHjxrFkyRJXjc1mIzMzk0mTJtG3b1+Cg4NJSEhwm7YhItKinD0Lr74KS5ZA5YOubrgBpk6FJ5+E66/3bPtERDzMo0F65cqVlz1usVhITEwk8TKPiW3dujUpKSmkpKTUWtOhQwdWr1592Wt17tyZ9evXX7amV69ebNu27bI1IiLNXkkJvPwyvPACfPONuc9uh+nTIT4errvOs+0TEfESXjdHWkREPOT4cXjpJVi2zAzTAN26wYwZMH481HC/h4iIL1OQFhHxdYcPw9Kl8Kc/mdM5AHr2hJkz4Wc/g1b6p0JEpCb621FExFd99RU89xysWgWVDx/o0weeeQYefBD8tAaHiMjlKEiLiPia/HxYuBBSU6Giwtx3331mgI6O1iO8RUTqSEFaRKQlSUwEf3+YPbv6sd/8BrZsgT17Lu4bNgxmzYJ77mmqFoqItBgK0iIiLYm/P8yZY349YwYYBpZt2+DXv764BrTFAo88Ys6B/uEPPddWEZFmTkFaRKQlqRyJnjMHv717ueeTT2j15ZfmPosFYmPNgN2jh+faKCLSQihIi4i0NL/9Lbz9Nv6rV3ND5b4f/Qjeegu6dvVgw0REWhbdki0i0pL84x9wxx2wezfGd7uMwED4+GOFaBGRBqYgLSLSEhw/bq75/MgjUFQEN96IBShv1QqLwwHz53u6hSIiLY6mdoiINHdvvw2TJplh2t8fBg6Ebdsof/ZZ1t91FyN27cK/8gbEmlbzEBGRelGQFhFproqKzAD997+b2xER5jJ2K1bAvHlUzJgBGzdS8cwz+FddzUNhWkSkQWhqh4hIc2MY8Oab5mO8//538xHes2dDbi507Ajz5lUPy7Nnm/vLyz3TZhGRFkgj0iIizUlhIUycCGlp5vadd8Jf/gJ33WVuJybW/r0aiRYRaVAakRYRaQ4MA954w1yRIy3NHIVOTDRX46gM0SIi0qQ0Ii0i4u0KCsy1od95x9z+wQ9g1SpzNFpERDxGI9IiIt7KMOC118xR6HfegYAAcxm7jz9WiBYR8QIakRYR8UZHjsCvfw0bNpjbP/yhOQrdq5dHmyUiIhdpRFpExJsYhhmY77jDDNGBgZCUBDk5CtEiIl5GI9IiIt7i8GH41a/g/ffN7R/9yFyR4447PNsuERGpkUakRUQ8zTBg5UozML//vjkKvWgRbN+uEC0i4sU0Ii0i4kmHDkF8PGRkmNv9+pmj0D16eLZdIiJyRRqRFhHxBMOAV181H+udkQGtW8Pzz8O//qUQLSLSTGhEWkSkqR08CP/f/webNpnbAwbAn/8M3bt7tl0iInJVNCItItJUKipgxQpzFHrTJnMUOjkZtm1TiBYRaYY0Ii0i0hT27zdHof/5T3P7nnvMUehbb/Vsu0REpN40Ii0i0pgqKuDll801oP/5TwgKgpdegq1bFaJFRJo5jUiLiDSWr76CCRPM0Axw333mMne33OLZdomISIPQiLSISEOrqICUFOjd2wzRbdqY2x9+qBAtItKCeDRIL1y4kB/96Ee0a9eO0NBQRo8ezZ49e9xq4uLisFgsbq/+/fu71ZSVlTF58mRCQkJo27Yto0aN4vDhw241xcXFxMbGYrPZsNlsxMbGcvLkSbeaQ4cOMXLkSNq2bUtISAhTpkzB4XC41ezevZuoqCiCgoK46aabmDdvHoZhNOCnIiLN2r//DYMGwZQpcPas+fXu3fDkk+CnsQsRkZbEo3+rb926lUmTJpGTk0NmZiYXLlwgJiaGM2fOuNUNHTqUgoIC12vjxo1ux6dOnUpaWhqpqalkZWVx+vRpRowYQXl5uatm3Lhx5OXlkZ6eTnp6Onl5ecTGxrqOl5eXM3z4cM6cOUNWVhapqamsXbuWadOmuWpKS0uJjo7Gbrezc+dOUlJSWLJkCcnJyY30CYlIs1FeDi++aI5Cf/QRtG0Lr7wCmzfD977n6daJiEgj8Ogc6fT0dLftv/zlL4SGhpKbm8t9993n2m+1WgkLC6vxHCUlJaxcuZLXXnuNwYMHA7B69WrCw8PZtGkTQ4YM4YsvviA9PZ2cnBz69esHwKuvvkpkZCR79uyhe/fuZGRk8Pnnn/P1119jt9sBWLp0KXFxcSQlJdG+fXtef/11zp8/z6pVq7BarURERLB3716Sk5NJSEjAYrE0xsckIt5u71544gnzYSoA999vzoXu2tWjzRIRkcblVf/PWFJSAkCHDh3c9m/ZsoXQ0FBuu+024uPjKSoqch3Lzc3F6XQSExPj2me324mIiGD79u0AZGdnY7PZXCEaoH///thsNreaiIgIV4gGGDJkCGVlZeTm5rpqoqKisFqtbjVHjx7lwIEDDfQpiEizUV4OS5fCnXeaIfq66+CPfzTXiFaIFhFp8bxm1Q7DMEhISOCee+4hIiLCtX/YsGE8+uijdOnShf379zN79mzuv/9+cnNzsVqtFBYWEhgYSHBwsNv5OnbsSGFhIQCFhYWEhoZWu2ZoaKhbTceOHd2OBwcHExgY6FbT9ZJ/HCu/p7CwkG7dulW7RllZGWVlZa7t0tJSAJxOJ06ns06fzbWovEZTXEu8j/q/EX35Jf7x8fjt2AFAxeDBlC9fDl26wIULHm6cSf3v29T/vk39f23q+rl5TZB+8skn+fTTT8nKynLbP3bsWNfXERER9O3bly5durBhwwYefvjhWs9nGIbbVIuapl00RE3ljYa1TetYuHAhc+fOrbY/IyODNm3a1Nr+hpaZmdlk1xLvo/5vOJbycr7/zjvcvmYNfk4nzqAg8p94gkODB8Nnn5kvL6P+923qf9+m/q+fs2fP1qnOK4L05MmTeffdd9m2bRs333zzZWs7depEly5d2LdvHwBhYWE4HA6Ki4vdRqWLiooYMGCAq+bYsWPVznX8+HHXiHJYWBg7vhtZqlRcXIzT6XSrqRydrnodoNpodqWZM2eSkJDg2i4tLSU8PJyYmBjat29/2ffaEJxOJ5mZmURHRxMQENDo1xPvov5vYJ9/bo5C79wJQMWQIfDKK0SEhxNxhW/1BPW/b1P/+zb1/7WpnEFwJR4N0oZhMHnyZNLS0tiyZUuNUyMudeLECb7++ms6deoEQJ8+fQgICCAzM5MxY8YAUFBQQH5+PosXLwYgMjKSkpISPv74Y+6++24AduzYQUlJiStsR0ZGkpSUREFBgevcGRkZWK1W+vTp46qZNWsWDoeDwMBAV43dbq825aOS1Wp1m1NdKSAgoEl/Yzf19cS7qP+v0YUL8PzzkG99jYoAACAASURBVJgIDgfYbPDCC/jFxeHXDG4yVv/7NvW/b1P/109dPzOP3mw4adIkVq9ezRtvvEG7du0oLCyksLCQc+fOAXD69GmmT59OdnY2Bw4cYMuWLYwcOZKQkBAeeughAGw2GxMmTGDatGls3ryZXbt28fjjj9OrVy/XKh49evRg6NChxMfHk5OTQ05ODvHx8YwYMYLu3bsDEBMTQ8+ePYmNjWXXrl1s3ryZ6dOnEx8f7xo5HjduHFarlbi4OPLz80lLS2PBggVasUOkJcvPh8hImDXLDNE/+Ym575e/BP25FxHxaR4N0suXL6ekpIRBgwbRqVMn1+vNN98EwN/fn927d/Pggw9y2223MX78eG677Tays7Np166d6zwvvPACo0ePZsyYMQwcOJA2bdrw3nvv4e/v76p5/fXX6dWrFzExMcTExNC7d29ee+0113F/f382bNhA69atGThwIGPGjGH06NEsWbLEVWOz2cjMzOTw4cP07duXiRMnkpCQ4DZ1Q0SaocREmD/ffZ/TCUlJ5oocn3wC118Pf/0rrF8PV5iCJiIivsHjUzsuJygoiA8++OCK52ndujUpKSmkpKTUWtOhQwdWr1592fN07tyZ9evXX7amV69ebNu27YptEpFmxN8f5swxv549Gz79FOLiYNcuc99tt5mP966yPKaIiIhX3GwoIuJRs2ebv86ZYwbmrCxzRBrgkUfg7bc1jUNERKrxqgeyiIh4zG9/C926mUG6MkQ/9RT8/e8K0SIiUiMFaRGRXbugb1/Yv//ivsBA+G7lHxERkZooSIuIb1uzBgYOhIMHoXIt+sBAc4WOS29AFBERqUJBWkR804ULMH06jBsH587BLbdAcTHMmwdlZeavc+YoTIuISK10s6GI+J4TJ+BnP4NNm8zte++Fjz4yw3PljYdVb0Csui0iIvIdBWkR8S3/93/w0EPmfOg2bWDVKvjsM4iOrh6WK7fLy5u8mSIi4v0UpEXEd7z1lvlEwrNnzRU61q2D3r3h0Udr/x6NRIuISC00R1pEWr7ycpgxA8aONUN0dLT5tMLevT3dMhERacYUpEWkZfv2Wxg+HJ57ztx+6inYuBE6dPBsu0REpNnT1A4Rabl274bRo+E//4GgIPjzn82bDEVERBqAgrSItEx//zvExcGZM9C1K6SlwQ9+4OlWiYhIC6KpHSLSspSXwzPPmDcQnjkD998PO3cqRIuISIPTiLSItBwnT5oPWHn/fXM7IcGcG91Kf9WJiEjD078uItIyfP65OR963z5o3Rr+53/gscc83SoREWnBFKRFpPlLS4Nf/AJOn4bOnc3tH/7Q060SEZEWTnOkRaT5qqiAZ5+Fhx82Q/SgQeb60ArRIiLSBDQiLSLNU0kJxMbCe++Z27/7HTz/PAQEeLZdIiLiMxSkRaT5+fJLcz70nj1gtcKf/mRO7RAREWlCCtIi0ry89555E+GpU3DzzeZ86L59Pd0qERHxQZojLSLNQ0UFzJsHo0aZIfree8350ArRIiLiIRqRFhHvV1oK48fDunXm9pNPQnKy5kOLiIhHKUiLiHfbu9ecD/3FFxAYCCtWwC9/6elWiYiIKEiLiBfbsMGcD11SAnY7/OMf0K+fp1slIiICaI60iHgjw4CkJBg50gzRAwdCbq5CtIiIeBWNSIuIdzl9GuLiYO1ac/s3v4GXXjKndYiIiHgRBWkR8R7//rc5H/qzz8wbCV9+GeLjPd0qERGRGilIi4h3SE+Hn/8cTp6ETp3MEenISE+3SkREpFaaIy0inmUYsGgR/OQnZoiOjDTXh1aIFhERL9fgQbqiooJDhw7VqXbhwoX86Ec/ol27doSGhjJ69Gj27NnjVmMYBomJidjtdoKCghg0aBCfffaZW01ZWRmTJ08mJCSEtm3bMmrUKA4fPuxWU1xcTGxsLDabDZvNRmxsLCdPnnSrOXToECNHjqRt27aEhIQwZcoUHA6HW83u3buJiooiKCiIm266iXnz5mEYRl0/HhGp6swZ+NnPYOZMM1DHx8OHH5ordIiIiHi5qw7S/v7+/O53v6OioqLG48ePH6dbt251OtfWrVuZNGkSOTk5ZGZmcuHCBWJiYjhz5oyrZvHixSQnJ7Ns2TJ27txJWFgY0dHRnDp1ylUzdepU0tLSSE1NJSsri9OnTzNixAjKy8tdNePGjSMvL4/09HTS09PJy8sjNjbWdby8vJzhw4dz5swZsrKySE1NZe3atUybNs1VU1paSnR0NHa7nZ07d5KSksKSJUtITk6u8+cnIt/5z3/MUee33jLnQ69YAX/6E1itnm6ZiIhI3RhXyWKxGO3atTMeeOAB48SJE9WOFxYWGhaL5WpPaxiGYRQVFRmAsXXrVsMwDKOiosIICwszFi1a5Ko5f/68YbPZjBUrVhiGYRgnT540AgICjNTUVFfNkSNHDD8/PyM9Pd0wDMP4/PPPDcDIyclx1WRnZxuA8eWXXxqGYRgbN240/Pz8jCNHjrhq1qxZY1itVqOkpMQwDMN45ZVXDJvNZpw/f95Vs3DhQsNutxsVFRV1eo8lJSUG4DpnY3M4HMa6desMh8PRJNcT7+K1/Z+RYRjBwYYBhtGxo2FkZXm6RS2S1/a/NAn1v29T/1+buua1qx6RtlgsZGZm8s0339C3b1/y8/NrrKmPkpISADp06ADA/v37KSwsJCYmxlVjtVqJiopi+/btAOTm5uJ0Ot1q7HY7ERERrprs7GxsNhv9qqxB279/f2w2m1tNREQE9ir/pTxkyBDKysrIzc111URFRWGtMmI2ZMgQjh49yoEDB+r1nkV8imHAkiUwdCgUF8Pdd5vrQw8c6OmWiYiIXLWrXrXDMAy6detGdnY2cXFxREZG8te//pWHH374mhpiGAYJCQncc889REREAFBYWAhAx44d3Wo7duzIwYMHXTWBgYEEBwdXq6n8/sLCQkJDQ6tdMzQ01K3m0usEBwcTGBjoVtO1a9dq16k8VtOUlrKyMsrKylzbpaWlADidTpxOZ20fR4OpvEZTXEu8j1f1/9mz+P/61/i9+SYAFXFxlP/hD9C6NXhD+1ogr+p/aXLqf9+m/r82df3c6r38XVBQEG+++SYLFy5k7NixzJo1i7lz59b3dDz55JN8+umnZGVlVTt26Qi3YRhXHPW+tKam+oaoMb670bC29ixcuLDGzyUjI4M2bdpc9j00pMzMzCa7lngfT/d/0LFj3L1oEdfv30+Fvz/5Eyawf9gw+Oc/PdouX+Hp/hfPUv/7NvV//Zw9e7ZOdde8jvTMmTO58847eeyxx/j00095/vnnr/ockydP5t1332Xbtm3cfPPNrv1hYWGAOdrbqVMn1/6ioiLXSHBYWBgOh4Pi4mK3UemioiIGDBjgqjl27Fi16x4/ftztPDt27HA7XlxcjNPpdKupHJ2ueh2oPmpeaebMmSQkJLi2S0tLCQ8PJyYmhvbt21/uY2kQTqeTzMxMoqOjCQgIaPTriXfxhv63/POf+M+aheXECYzQUCrWrKHHvffSwyOt8S3e0P/iOep/36b+vzaVMwiu5KqDdE0jrz/5yU/YsWMHo0ePZvDgwXU+l2EYTJ48mbS0NLZs2VJtakS3bt0ICwsjMzOTu+66CwCHw8HWrVt57rnnAOjTpw8BAQFkZmYyZswYAAoKCsjPz2fx4sUAREZGUlJSwscff8zdd98NwI4dOygpKXGF7cjISJKSkigoKHCF9oyMDKxWK3369HHVzJo1C4fDQeB3jyvOyMjAbrdXm/JRyWq1us2prhQQENCkv7Gb+nriXTzS/4YBL74I06dDRQX07YvlH/+gVXh407ZD9Offx6n/fZv6v37q+pld9c2GRi1rJt92223s2LGDXr161flckyZNYvXq1bzxxhu0a9eOwsJCCgsLOXfuHGCG9qlTp7JgwQLS0tLIz88nLi6ONm3aMG7cOABsNhsTJkxg2rRpbN68mV27dvH444/Tq1cvV6jv0aMHQ4cOJT4+npycHHJycoiPj2fEiBF0794dgJiYGHr27ElsbCy7du1i8+bNTJ8+nfj4eNfI8bhx47BarcTFxZGfn09aWhoLFiwgISGh3jdYirRI585BbCwkJJghevx42LYNFKJFRKQFueoR6drWjwZo164d7733Xp0fyLJ8+XIABg0a5Lb/L3/5C3FxcQD8/ve/59y5c0ycOJHi4mL69etHRkYG7dq1c9W/8MILtGrVijFjxnDu3DkeeOABVq1ahb+/v6vm9ddfZ8qUKa7VPUaNGsWyZctcx/39/dmwYQMTJ05k4MCBBAUFMW7cOJYsWeKqsdlsZGZmMmnSJPr27UtwcDAJCQluUzdEfN6hQ/DQQ/C//wv+/pCcDJMng37YFBGRFuaa50jXpHPnznWqq210uyqLxUJiYiKJiYm11rRu3ZqUlBRSUlJqrenQoQOrV6++7LU6d+7M+vXrL1vTq1cvtm3bdtkakRYvMdEMybNnu+/fsgWGD4ezZyEkBN5+Gy75QVlERKSlqHeQ9vPzu+x0hqpPFRSRFsbfH+bMMb+ePducD52SAlOnml936gTZ2dCli2fbKSIi0ojqHaTT0tLctp1OJ7t27eKvf/3rNS2DJyLNQOVI9Jw5cOECHDwIf/2rua93b8jJgaAgz7VPRESkCdQ7SD/44IPV9v30pz/ljjvu4M0332TChAnX1DAR8XKzZ5s3Fc6bd3Hf0KGwcaPmQ4uIiE+46lU7rqRfv35s2rSpoU8rIt7myBF4772L2wEB8P77CtEiIuIzGjRInzt3jpSUFLeHqohIC/TllzBgAOTnm9sBAeZjvufP92y7REREmlC9p3YEBwe73WxoGAanTp2iTZs2V1wdQ0SasR07zJU5Tpwwt//rv8wl7ubPd78BUUREpIWrd5B+8cUX3bb9/Py48cYb6devn9ujukWkBXn/ffjpT83l7QBmzICFC82vq96AWHVbRESkhap3kB4/fnxDtkNEvN1rr8ETT5irdHz/+/Dzn1efylEZnrX8pYiI+IBreiDL+fPn+fTTTykqKqr2xMNRo0ZdU8NExIssWQJPPWV+/fjjsHIlBAbWXKuRaBER8RH1DtLp6enExsZyonKeZBUWi0UPZBFpCSoq4Pe/h6VLze2EBHj+efBr8AV/REREmp16/2v45JNPMmbMGAoKCqioqHB7KUSLtABOJ8TFXQzRixebXytEi4iIANcwIl1UVERCQgIdO3ZsyPaIiDc4cwYefdS8udDfH/78Z/jFLzzdKhEREa9S76Gln/70p2zZsqUBmyIiXuGbb+D++80QHRQE776rEC0iIlKDeo9IL1u2jEcffZSPPvqIXr16ERAQ4HZ8ypQp19w4EWliBw/CkCGwZw906AAbNkD//p5ulYiIiFeqd5B+4403+OCDDwgKCmLLli1uD2exWCwK0iLNTX6+GaKPHoXwcPjgA+jRw9OtEhER8Vr1DtL//d//zbx585gxYwZ+uvlIpHnLyoKRI+HkSejZ0wzRN9/s6VaJiIh4tXonYIfDwdixYxWiRZq7d9+F6GgzRA8cCB99pBAtIiJSB/VOwePHj+fNN99syLaISFNbuRIeegjOnzdHpDMyzLnRIiIickX1ntpRXl7O4sWL+eCDD+jdu3e1mw2Tk5OvuXEi0kgMAxYuhGeeMbefeAL++EdodU0POxUREfEp9f5Xc/fu3dx1110A5Ofnux2reuOhiHiZigqYOhVSUsztmTMhKQn051ZEROSq1DtIf/jhhw3ZDhFpCmVlMH48VE7Leukl0Ao7IiIi9XLVc6Ttdju//e1vSU9Px+FwNEabRKQxnDoFw4ebITogANasUYgWERG5BlcdpN944w3atGnD5MmTCQkJ4dFHH+W1117j22+/bYz2iUhDKCqCQYNg82a47jrzQSs/+5mnWyUiItKsXXWQHjRoEEuXLmXfvn1kZ2fzwx/+kJdffplOnToxaNAgXnjhBb766qvGaKuI1EObwkJaDRoE//u/cOON8OGH5nJ3IiIick2uaRHoO+64g5kzZ5KTk8OhQ4d47LHH+Oc//0mvXr2IiIhgw4YNDdVOEamPvDzunTEDy7//DV27wr/+BX37erpVIiIiLUKDrXXVsWNH4uPjiY+P5+zZs3zwwQdYrdaGOr2IXK0PP6TVgw8ScOoURu/eWNLToVMnT7dKRESkxbjqIF1RUcHSpUtZt24dTqeTwYMHM2fOHFq3bu2qadOmDQ899FCDNlRErsLf/w6PPYbF4eCbO+7AtnkzASEhnm6ViIhIi3LVUzuee+45ZsyYQdu2benUqRPJyclM0Z3/It5j+XIYMwYcDipGjyb72WfBZvN0q0RERFqcqw7Sq1atIiUlhYyMDN555x3WrVvH3/72NwzDaIz2iUhdGQY8+yxMnGh+/ZvfUL5mDRWBgZ5umYiISIt01UH64MGDjBgxwrU9ZMgQDMPg6NGjV33xbdu2MXLkSOx2OxaLhXXr1rkdj4uLw2KxuL369+/vVlNWVuZaiq9t27aMGjWKw4cPu9UUFxcTGxuLzWbDZrMRGxvLyZMn3WoOHTrEyJEjadu2LSEhIUyZMqXaOtm7d+8mKiqKoKAgbrrpJubNm6cfIMQ7lJfDb34D8+aZ24mJ8Mor4O/v0WaJiIi0ZFcdpB0OB0FBQa5ti8VCYGAgZWVlV33xM2fOcOedd7Js2bJaa4YOHUpBQYHrtXHjRrfjU6dOJS0tjdTUVLKysjh9+jQjRoygvLzcVTNu3Djy8vJIT08nPT2dvLw8YmNjXcfLy8sZPnw4Z86cISsri9TUVNauXcu0adNcNaWlpURHR2O329m5cycpKSksWbKE5OTkq37fIg3q/Hl49FH405/Mx3wvX26OTOuR3yIiIo2qXqt2zJ49mzZt2ri2HQ4HSUlJ2KrMw6xLwBw2bBjDhg27bI3VaiUsLKzGYyUlJaxcuZLXXnuNwYMHA7B69WrCw8PZtGkTQ4YM4YsvviA9PZ2cnBz69esHwKuvvkpkZCR79uyhe/fuZGRk8Pnnn/P1119jt9sBWLp0KXFxcSQlJdG+fXtef/11zp8/z6pVq7BarURERLB3716Sk5NJSEjAotAinnDyJDz4IGzbBoGB8MYb8Mgjnm6ViIiIT7jqEen77ruPPXv2sGvXLtdrwIAB/Oc//3Ft5+XlNVgDt2zZQmhoKLfddhvx8fEUFRW5juXm5uJ0OomJiXHts9vtREREsH37dgCys7Ox2WyuEA3Qv39/bDabW01ERIQrRIM5ZaWsrIzc3FxXTVRUlNuSfkOGDOHo0aMcOHCgwd6vSJ0dPQpRUWaIbt8ePvhAIVpERKQJXfWI9JYtWxqhGTUbNmwYjz76KF26dGH//v3Mnj2b+++/n9zcXKxWK4WFhQQGBhIcHOz2fR07dqSwsBCAwsJCQkNDq507NDTUraZjx45ux4ODgwkMDHSr6dq1a7XrVB7r1q1bje+hrKzMbdpLaWkpAE6nE6fTWdePot4qr9EU15ImtHcvrUaMwHLgAEZYGBfeew/uvBMu6Wf1v29T//s29b9vU/9fm7p+bg32QJbGMHbsWNfXERER9O3bly5durBhwwYefvjhWr/PMAy3qRY1TbtoiJrKGw0vN61j4cKFzJ07t9r+jIwMt+kxjS0zM7PJriWN6/p9++g/fz6W0lJOd+pEdmIiZ48cgSNHav0e9b9vU//7NvW/b1P/18/Zs2frVFevIH3u3Dlyc3Pp0KEDPXv2dDt2/vx53nrrLX7xi1/U59SX1alTJ7p06cK+ffsACAsLw+FwUFxc7DYqXVRUxIABA1w1x44dq3au48ePu0aUw8LC2LFjh9vx4uJinE6nW03l6HTV6wDVRrOrmjlzJgkJCa7t0tJSwsPDiYmJoX379nV+7/XldDrJzMwkOjqagICARr+eNC5LZib+iYlYzpyh4oc/xPruuwyq4X9cKqn/fZv637ep/32b+v/aVM4guJKrDtJ79+4lJiaGQ4cOYbFYuPfee1mzZg2dvnv0cElJCb/85S8bJUifOHGCr7/+2nWtPn36EBAQQGZmJmPGjAGgoKCA/Px8Fi9eDEBkZCQlJSV8/PHH3H333QDs2LGDkpISV9iOjIwkKSmJgoIC17kzMjKwWq306dPHVTNr1iwcDgeB363Lm5GRgd1urzbloyqr1Vrjo9IDAgKa9Dd2U19PGsEbb8D48XDhAgwejN8//oFfu3Z1+lb1v29T//s29b9vU//XT10/s6u+2fDpp5+mV69eFBUVsWfPHtq3b8/AgQM5dOjQVTfy9OnT5OXluW5O3L9/P3l5eRw6dIjTp08zffp0srOzOXDgAFu2bGHkyJGEhIS4Hj9us9mYMGEC06ZNY/PmzezatYvHH3+cXr16uVbx6NGjB0OHDiU+Pp6cnBxycnKIj49nxIgRdO/eHYCYmBh69uxJbGwsu3btYvPmzUyfPp34+HjXqPG4ceOwWq3ExcWRn59PWloaCxYs0Iod0jRefBEee8wM0T/7GWzYAHUM0SIiItJIjKsUGhpqfPrpp277Jk6caHTu3Nn46quvjMLCQsPPz69O5/rwww8NoNpr/PjxxtmzZ42YmBjjxhtvNAICAozOnTsb48ePNw4dOuR2jnPnzhlPPvmk0aFDByMoKMgYMWJEtZoTJ04Yjz32mNGuXTujXbt2xmOPPWYUFxe71Rw8eNAYPny4ERQUZHTo0MF48sknjfPnz7vVfPrpp8a9995rWK1WIywszEhMTDQqKirq+tEZhmEYJSUlBmCUlJRc1ffVl8PhMNatW2c4HI4muZ40sIoKw3j6acMwn1VoGFOmGEZ5eZ2/Xf3v29T/vk3979vU/9emrnntqqd2nDt3jlat3L/t5Zdfxs/Pj6ioKN544406n2vQoEGXfTLgBx98cMVztG7dmpSUFFJSUmqt6dChA6tXr77seTp37sz69esvW9OrVy+2bdt2xTaJNIgLFyA+HlatMrcXLoSnn9aDVkRERLzEVQfp22+/nU8++YQePXq47U9JScEwDEaNGtVgjRPxWWfPwtixsH69+ZjvP/0JnnjC060SERGRKq56jvRDDz3EmjVrajy2bNkyfv7zn192lFlEruDbb2HwYDNEt24NaWkK0SIiIl7oqoP0zJkz2bhxY63HX3nlFSoqKq6pUSI+6+uv4Z57IDsbrr8eNm2CkSM93SoRERGpgVc/kEXEp3z+OQwZAocPw003mY/8vuMOT7dKREREanHVI9Ii0giys82R6MOH4fbbYft2hWgREREvpyAt0lQSE2H+/Or7N2yA++6D4mLo3x+ysqBz5yZvnoiIiFwdBWmRpuLvD3PmuIfpVavMOdAXLsCtt5pzom+4wWNNFBERkbrTHGmRpjJ7tvnrnDnm41WsVpgxw9z3gx/Axx+DHuMqIiLSbChIizSl2bPNEP3ssxf33XMPbNumB62IiIg0MwrSIk3JMODkyYvb/v7w0Ueea4+IiIjUm+ZIizQVw4Bp0+CFF8ztVq2gvLzmGxBFRETE6ylIizQFw4Dp0y+G6FGjwOmEefOq34AoIiIizYKmdog0tsoQnZxsbo8aBe+8Y35d9QbEqtsiIiLi9RSkRRqTYcBTT10M0SNHXgzRlSrDc3l507ZNREREromCtEhjqQzRS5ea2ytWwK9/XXOtRqJFRESaHc2RFmkMhgG///3FEL18ee0hWkRERJolBWmRhlYZopcsMbeXL4ff/MazbRIREZEGpyAt0pAMA55++mKIfuUVhWgREZEWSkFapKFUhujnnze3X3kFfvtbz7ZJREREGo2CtEhDMAyYMeNiiH75ZYVoERGRFk5BWuRaVYboxYvN7ZdfhokTPdsmERERaXQK0iLXwjBg5syLIXrZMoVoERERH6EgLVJflSH6uefM7WXLYNIkz7ZJREREmoyCtEh9GAbMmqUQLSIi4sMUpEWuVmWIXrTI3FaIFhER8UkK0iJXwzDgmWcuhuiUFIVoERERH6UgLVJXlSF64UJzOyUFnnzSs20SERERj1GQFqkLw4D//u+LIfoPf1CIFhER8XEK0iJXUhmiFywwt//wB5g82bNtEhEREY/zaJDetm0bI0eOxG63Y7FYWLdundtxwzBITEzEbrcTFBTEoEGD+Oyzz9xqysrKmDx5MiEhIbRt25ZRo0Zx+PBht5ri4mJiY2Ox2WzYbDZiY2M5efKkW82hQ4cYOXIkbdu2JSQkhClTpuBwONxqdu/eTVRUFEFBQdx0003MmzcPwzAa8BMRr3NpiH7pJYVoERERATwcpM+cOcOdd97JsmXLajy+ePFikpOTWbZsGTt37iQsLIzo6GhOnTrlqpk6dSppaWmkpqaSlZXF6dOnGTFiBOXl5a6acePGkZeXR3p6Ounp6eTl5REbG+s6Xl5ezvDhwzlz5gxZWVmkpqaydu1apk2b5qopLS0lOjoau93Ozp07SUlJYcmSJSQnJzfCJyNewTBg9mz3ED1limfbJCIiIt7D8BKAkZaW5tquqKgwwsLCjEWLFrn2nT9/3rDZbMaKFSsMwzCMkydPGgEBAUZqaqqr5siRI4afn5+Rnp5uGIZhfP755wZg5OTkuGqys7MNwPjyyy8NwzCMjRs3Gn5+fsaRI0dcNWvWrDGsVqtRUlJiGIZhvPLKK4bNZjPOnz/vqlm4cKFht9uNioqKOr/PkpISA3Cdt7E5HA5j3bp1hsPhaJLrtRgVFYbxzDOGYcZpw3jxRU+3qF7U/75N/e/b1P++Tf1/beqa17x2jvT+/fspLCwkJibGtc9qtRIVFcX27dsByM3Nxel0utXY7XYiIiJcNdnZ2dhsNvr16+eq6d+/Pzabza0mIiICu93uqhkyZAhlZWXk5ua6aqKiorBarW41R48e5cCBAw3/AYjnGAbMmQNJSeb2iy/C737n2TaJiIiI12nl6QbUprCwEICOHTu67e/YsSMHDx501QQGBhIc6h7bsAAAIABJREFUHFytpvL7CwsLCQ0NrXb+0NBQt5pLrxMcHExgYKBbTdeuXatdp/JYt27danwfZWVllJWVubZLS0sBcDqdOJ3OWt59w6m8RlNcq0UwDPwSE/H/bnWO8iVLqJg4EZrp56f+923qf9+m/vdt6v9rU9fPzWuDdCWLxeK2bRhGtX2XurSmpvqGqDG+u9Hwcu1ZuHAhc+fOrbY/IyODNm3aXOZdNKzMzMwmu1azZRjcvmYN3d96C4DdTzzBf265BTZu9HDDrp3637ep/32b+t+3qf/r5+zZs3Wq89ogHRYWBpijvZ06dXLtLyoqco0Eh4WF4XA4KC4udhuVLioqYsCAAa6aY8eOVTv/8ePH3c6zY8cOt+PFxcU4nU63msrR6arXgeqj5lXNnDmThIQE13ZpaSnh4eHExMTQvn37K3wK187pdJKZmUl0dDQBAQGNfr1myzDwmzsX/+9CdPnzz3P7737H7R5u1rVS//s29b9vU//7NvX/tamcQXAlXhuku3XrRlhYGJmZmdx1110AOBwOtm7dynPPPQdAnz59CAgIIDMzkzFjxgBQUFBAfn4+ixcvBiAyMpKSkhI+/vhj7r77bgB27NhBSUmJK2xHRkaSlJREQUGBK7RnZGRgtVrp06ePq2bWrFk4HA4CAwNdNXa7vdqUj6qsVqvbvOpKAQEBTfobu6mv16wYBiQmXlydIzkZ///6r/+/vTuPj6q89zj+HSYLIYRpIpCYGhBkEU2gGBSCVVkDlkUtFTQ0hatgkU2K6GWpkiAQUFxarGitBSvS6C3C5QJCAshiAZFIFERxA+JCjEtIAoRkSJ77xzQThkCEgeRMMp/365UXc855MvOb8zv4+nJ85hnZLS3q0qL//o3++zf679/ov3fO95xZ+mHDY8eOKTs7W9nZ2ZJcHzDMzs5WTk6ObDabJk2apLlz52rFihXat2+fRo4cqUaNGikpKUmS5HA4dO+99+rBBx/Uxo0btWfPHv32t79VXFyc+vTpI0nq0KGD+vfvr9GjR2vnzp3auXOnRo8erYEDB6p9+/aSpMTERF1zzTVKTk7Wnj17tHHjRk2ZMkWjR4923zVOSkpScHCwRo4cqX379mnFihWaO3euJk+e/JNTTeDjUlKkWbNcj598UvrDHywtBwAA1A2W3pHevXu3evbs6d6umAIxYsQILVmyRA8//LCKi4s1duxY5efnq2vXrsrIyFBYWJj7d55++mkFBARo6NChKi4uVu/evbVkyRLZ7ZX3E1999VVNnDjRvbrH4MGDPdauttvtWrNmjcaOHasbb7xRISEhSkpK0oIFC9xjHA6HMjMzNW7cOHXp0kXh4eGaPHmyx7QN1EFnhmj6CQAAzpOlQbpHjx7VfjOgzWZTSkqKUlJSzjmmYcOGWrhwoRYuXHjOMREREVq6dGm1tbRo0UKrV6+udkxcXJy2bt1a7RjUISkpUsUHQRcsIEQDAIAL4rPrSAM16swQfdq3WAIAAJwPgjT8T2pqZYh+4glCNAAA8ApBGv4lNdV1N1pyhegpUywtBwAA1F0EafiPWbMqQ/TjjxOiAQDARSFIwz/MmiXNnOl6/Pjj0kMPWVsPAACo8wjSqP8ee6wyRM+fT4gGAACXBEEa9dtjj0mPPup6PH++9PDD1tYDAADqDYI06q/ZsytD9Lx5hGgAAHBJEaRRP82eLT3yiOvxvHnSf/+3tfUAAIB6hyCN+mfOnMoQnZZGiAYAADWCII36Zc4c6Y9/dD1OS5OmTrW2HgAAUG8RpFF/zJ1LiAYAALWGII36Ye5cacaMyseEaAAAUMMI0qj70tI8Q/S0adbWAwAA/AJBGnVbWpo0fbrr8Zw5hGgAAFBrCNKou+bN8wzRFY8BAABqAUEaddO8eZV3n2fPJkQDAIBaR5CGb0tJcX3N9+nmz68M0T17Vs6PBgAAqEUEafg2u931Nd8VYXr+fM8VOXr2tKYuAADg9wKsLgCoVsU3FD76qLRtm5SZWXls1qzK4wAAALWMIA3f98gj0o4d0ptvVu4jRAMAAIsxtQO+7/nnPUN0UBAhGgAAWI4gDd/28svS/fdXbgcFSaWlVT+ACAAAUMsI0vBdr70m3XNP5XZqqlRS4prWcfoHEAEAACzAHGn4ppUrpeHDpfJy13Zqqis8S54fQDx9GwAAoBYRpOF73nxTGjpUKiuTOnaUhgypDM0VKsJzWVnt1wcAACCCNHzNpk3Sr38tOZ3SnXdKy5ZJAee4TLkTDQAALMQcafiOf/9bGjRIOnlSGjxYevXVc4doAAAAixGk4RvefVe69VbpxAkpMVF6/XUpMNDqqgAAAM7Jp4N0SkqKbDabx09UVJT7uDFGKSkpio6OVkhIiHr06KEPP/zQ4zlKSko0YcIENW3aVKGhoRo8eLC++uorjzH5+flKTk6Ww+GQw+FQcnKyjh496jEmJydHgwYNUmhoqJo2baqJEyeqtLS05t68P3n/falfP6moSOrRQ1qxQgoOtroqAACAavl0kJaka6+9VkeOHHH/7N27133s8ccf11NPPaVnn31W7777rqKiotS3b18VFRW5x0yaNEkrVqxQenq63n77bR07dkwDBw5U2WkfUktKSlJ2drbWrVundevWKTs7W8nJye7jZWVlGjBggI4fP663335b6enpWr58uR588MHaOQn12f79Up8+Un6+lJAg/d//SY0aWV0VAADAT/L5CagBAQEed6ErGGP0zDPPaMaMGfr1r38tSXr55ZcVGRmpZcuW6fe//70KCgr00ksv6ZVXXlGfPn0kSUuXLlVMTIw2bNigfv366aOPPtK6deu0c+dOde3aVZL04osvKiEhQQcOHFD79u2VkZGh/fv368svv1R0dLQk6cknn9TIkSM1Z84cNWnSpJbORj3z2WeuEP3991J8vGu1jsaNra4KAADgvPj8HelPP/1U0dHRatWqle666y598cUXkqSDBw8qNzdXiYmJ7rHBwcG65ZZbtH37dklSVlaWnE6nx5jo6GjFxsa6x+zYsUMOh8MdoiWpW7ducjgcHmNiY2PdIVqS+vXrp5KSEmVlZdXcm6/PDh2SevWSjhyR4uKk9eslh8PqqgAAAM6bT9+R7tq1q/7xj3+oXbt2+vbbbzV79mx1795dH374oXJzcyVJkZGRHr8TGRmpw4cPS5Jyc3MVFBSk8PDwKmMqfj83N1fNmzev8trNmzf3GHPm64SHhysoKMg95lxKSkpUUlLi3i4sLJQkOZ1OOZ3OnzwHF6viNWrjtc7b118roHdv2b78UqZ9e51au1Zq0sS15B0uKZ/sP2oN/fdv9N+/0f+Lc77nzaeD9K233up+HBcXp4SEBF111VV6+eWX1a1bN0mSzWbz+B1jTJV9ZzpzzNnGezPmbNLS0pSamlplf0ZGhhrV4lzgzMzMWnut6gQfPaobZ8xQ2Ndf63hkpN5++GGd5K5+jfOV/sMa9N+/0X//Rv+9c+LEifMa59NB+kyhoaGKi4vTp59+qttvv12S627x5Zdf7h6Tl5fnvnscFRWl0tJS5efne9yVzsvLU/fu3d1jvv322yqv9d1333k8zzvvvONxPD8/X06ns8qd6jNNmzZNkydPdm8XFhYqJiZGiYmJtTK32ul0KjMzU3379lWg1cvJ/fCDAvr2le3rr2VatFDQxo3q1bKltTXVcz7Vf9Q6+u/f6L9/o/8Xp2IGwU+pU0G6pKREH330kW666Sa1atVKUVFRyszMVOfOnSVJpaWl2rJli+bPny9Jio+PV2BgoDIzMzV06FBJ0pEjR7Rv3z49/vjjkqSEhAQVFBRo165duuGGGyRJ77zzjgoKCtxhOyEhQXPmzNGRI0fcoT0jI0PBwcGKj4+vtubg4GAFn2Upt8DAwFq9sGv79ao4elQaMEDat0+6/HLZNm5UYJs21tXjZyzvPyxF//0b/fdv9N8753vOfDpIT5kyRYMGDVKLFi2Ul5en2bNnq7CwUCNGjJDNZtOkSZM0d+5ctW3bVm3bttXcuXPVqFEjJSUlSZIcDofuvfdePfjgg7rssssUERGhKVOmKC4uzr2KR4cOHdS/f3+NHj1aL7zwgiTpvvvu08CBA9W+fXtJUmJioq655holJyfriSee0I8//qgpU6Zo9OjRrNhxPoqKXF+28t57UrNm0saNEiEaAADUcT4dpL/66ivdfffd+v7779WsWTN169ZNO3fuVMv/TAd4+OGHVVxcrLFjxyo/P19du3ZVRkaGwsLC3M/x9NNPKyAgQEOHDlVxcbF69+6tJUuWyG63u8e8+uqrmjhxont1j8GDB+vZZ591H7fb7VqzZo3Gjh2rG2+8USEhIUpKStKCBQtq6UzUYSdOuL72e+dOKTxcysyUOnSwuioAAICL5tNBOj09vdrjNptNKSkpSklJOeeYhg0bauHChVq4cOE5x0RERGjp0qXVvlaLFi20evXqasfgDCdPSnfcIW3Z4lqVIyND6tTJ6qoAAAAuCZ9fRxp1VGmpNHSoKzyHhrq+bKVLF6urAgAAuGQI0rj0Tp2Shg93fd13w4auP//zwU0AAID6giCNS6usTPqv/5L+9S8pKEhasULq2dPqqgAAAC45gjQuHWOkMWOkpUulgADp9del/v2trgoAAKBGEKRxaRgjPfCA9Le/SQ0auML0bbdZXRUAAECNIUjj4hkjTZ0qVayM8ve/S8OGWVsTAABADSNI4+Klpkr/+aZIPf+8NGKEtfUAAADUAoI0Ls78+a4gLUnPPCP9/vfW1gMAAFBLCNLw3p//7JrSIUlpaa450gAAAH6CIA3v/PWvlcH50UcrAzUAAICfIEjjwr3yimuZO0maMkWq5ivaAQAA6iuCNC7M//yPNHKka6WO8eNdHzK02ayuCgAAoNYRpHH+Vq2SkpKk8nJp1CjpT38iRAMAAL9FkMb5Wb9euvNO6dQpafhw1zJ3Dbh8AACA/yIJ4adt3izdfrtUWioNGSItWSLZ7VZXBQAAYCmCNKq3Y4c0cKB08qTrz2XLpIAAq6sCAACwHEEa55aVJfXvLx0/LvXt6/qgYVCQ1VUBAAD4BII0zu6DD6TERKmwULr5ZmnlSqlhQ6urAgAA8BkEaVT18cdSnz7Sjz9KXbtKq1dLjRpZXRUAAIBPIUjD0+efS717S999J3XuLK1bJ4WFWV0VAACAzyFIo1JOjtSrl/TNN1JsrJSRIf3sZ1ZXBQAA4JMI0nD55htXiM7Jkdq1kzIzpaZNra4KAADAZxGkIeXluaZzfP651KqVtHGjFBVldVUAAAA+jSDt73780bW03ccfS1dcIW3a5PoTAAAA1SJI+7OCAqlfP9dSd1FRrhB95ZVWVwUAAFAnEKT91bFj0q9+Je3e7ZoLvXGj1Lat1VUBAADUGQRpf1RcLA0eLG3f7lqVIzNTuuYaq6sCAACoUwjS/qakRLrjDumtt1zrQ69fL/3iF1ZXBQAAUOcQpP2J0ykNG+YKz40aSWvXSjfcYHVVAAAAdRJB2l+UlUnJydL//q8UHCytWiX98pdWVwUAAFBnEaS98Nxzz6lVq1Zq2LCh4uPjtW3bNqtL8pSSIj32WOV2ebnso0dLr70mNWggDRniWjcaAAAAXiNIX6DXXntNkyZN0owZM7Rnzx7ddNNNuvXWW5WTk2N1aZXsdunRR11h2hh1fOEFNVi6VLLZpPJy6eqrra4QAACgziNIX6CnnnpK9957r0aNGqUOHTromWeeUUxMjBYtWmR1aZUeeUSaNUt69FHZb75Zrdavl5EkY1z7H3nE6goBAADqvACrC6hLSktLlZWVpalTp3rsT0xM1Pbt28/6OyUlJSopKXFvFxYWSpKcTqecTmfNFTt1quxbt6rBhg2SJJukspkzVT51qutDh/ALFddYjV5r8Fn037/Rf/9G/y/O+Z43gvQF+P7771VWVqbIyEiP/ZGRkcrNzT3r76SlpSk1NbXK/oyMDDVq1KhG6qwQcued6rthgytEBwRodefOrpU64HcyMzOtLgEWov/+jf77N/rvnRMnTpzXOIK0F2w2m8e2MabKvgrTpk3T5MmT3duFhYWKiYlRYmKimjRpUqN1Npgzxx2i7adOaeCePSqfMaNGXxO+xel0KjMzU3379lVgYKDV5aCW0X//Rv/9G/2/OBUzCH4KQfoCNG3aVHa7vcrd57y8vCp3qSsEBwcrODi4yv7AwMCavbAfe0xKTVXZzJla3bmzBu7ZI3tqqux2O3Ok/VCNX2/wafTfv9F//0b/vXO+54wPG16AoKAgxcfHV/nfJJmZmerevbtFVZ3FY4+5Vu2YNct9B7p8xgz3BxA9lsYDAACAV7gjfYEmT56s5ORkdenSRQkJCfrrX/+qnJwcjRkzxurSKpWVVa7Ocfpk+Yo70WVl1tQFAABQjxCkL9CwYcP0ww8/aNasWTpy5IhiY2O1du1atWzZ0urSKqWknPsY0zoAAAAuCYK0F8aOHauxY8daXQYAAAAsxBxpAAAAwAsEaQAAAMALBGkAAADACwRpAAAAwAsEaQAAAMALBGkAAADACyx/V8uMMZLO/zvcL5bT6dSJEydUWFjIV4T6Ifrv3+i/f6P//o3+X5yKnFaR286FIF3LioqKJEkxMTEWVwIAAIDqFBUVyeFwnPO4zfxU1MYlVV5erm+++UZhYWGy2Ww1/nqFhYWKiYnRl19+qSZNmtT468G30H//Rv/9G/33b/T/4hhjVFRUpOjoaDVocO6Z0NyRrmUNGjTQFVdcUeuv26RJE/4i+TH679/ov3+j//6N/nuvujvRFfiwIQAAAOAFgjQAAADgBXtKSkqK1UWgZtntdvXo0UMBAczk8Uf037/Rf/9G//0b/a95fNgQAAAA8AJTOwAAAAAvEKQBAAAALxCkAQAAAC8QpAEAAAAvEKTrseeee06tWrVSw4YNFR8fr23btlldErywdetWDRo0SNHR0bLZbFq5cqXHcWOMUlJSFB0drZCQEPXo0UMffvihx5iSkhJNmDBBTZs2VWhoqAYPHqyvvvrKY0x+fr6Sk5PlcDjkcDiUnJyso0eP1vj7w7mlpaXp+uuvV1hYmJo3b67bb79dBw4c8BhD/+uvRYsWqWPHju4v1EhISNCbb77pPk7v/UtaWppsNpsmTZrk3sc14AMM6qX09HQTGBhoXnzxRbN//37zwAMPmNDQUHP48GGrS8MFWrt2rZkxY4ZZvny5kWRWrFjhcXzevHkmLCzMLF++3Ozdu9cMGzbMXH755aawsNA9ZsyYMebnP/+5yczMNO+9957p2bOn6dSpkzl16pR7TP/+/U1sbKzZvn272b59u4mNjTUDBw6stfeJqvr162cWL15s9u3bZ7Kzs82AAQNMixYtzLFjx9xj6H/9tWrVKrNmzRpz4MABc+DAATN9+nQTGBho9u3bZ4yh9/5k165d5sorrzQdO3Y0DzzwgHs/14D1CNL11A033GDGjBnjse/qq682U6dOtagiXApnBuny8nITFRVl5s2b59538uRJ43A4zPPPP2+MMebo0aMmMDDQpKenu8d8/fXXpkGDBmbdunXGGGP2799vJJmdO3e6x+zYscNIMh9//HFNvy2cp7y8PCPJbNmyxRhD//1ReHi4+dvf/kbv/UhRUZFp27atyczMNLfccos7SHMN+AamdtRDpaWlysrKUmJiosf+xMREbd++3aKqUBMOHjyo3Nxcj14HBwfrlltucfc6KytLTqfTY0x0dLRiY2PdY3bs2CGHw6GuXbu6x3Tr1k0Oh4NrxocUFBRIkiIiIiTRf39SVlam9PR0HT9+XAkJCfTej4wbN04DBgxQnz59PPZzDfgGvuqmHvr+++9VVlamyMhIj/2RkZHKzc21qCrUhIp+nq3Xhw8fdo8JCgpSeHh4lTEVv5+bm6vmzZtXef7mzZtzzfgIY4wmT56sX/7yl4qNjZVE//3B3r17lZCQoJMnT6px48ZasWKFrrnmGnfAoff1W3p6urKysrR79+4qx/j77xsI0vWYzWbz2DbGVNmH+sGbXp855mzjuWZ8x/jx4/XBBx/o7bffrnKM/tdf7du3V3Z2to4eParly5drxIgR2rJli/s4va+/vvzySz3wwAPKyMhQw4YNzzmOa8BaTO2oh5o2bSq73V7lX5J5eXlV/uWKui0qKkqSqu11VFSUSktLlZ+fX+2Yb7/9tsrzf/fdd1wzPmDChAlatWqV3nrrLV1xxRXu/fS//gsKClKbNm3UpUsXpaWlqVOnTvrTn/5E7/1AVlaW8vLyFB8fr4CAAAUEBGjLli3685//rICAAHd/uAasRZCuh4KCghQfH6/MzEyP/ZmZmerevbtFVaEmtGrVSlFRUR69Li0t1ZYtW9y9jo+PV2BgoMeYI0eOaN++fe4xCQkJKigo0K5du9xj3nnnHRUUFHDNWMgYo/Hjx+uNN97Qpk2b1KpVK4/j9N//GGNUUlJC7/1A7969tXfvXmVnZ7t/unTpouHDhys7O1utW7fmGvAFtf/5RtSGiuXvXnrpJbN//34zadIkExoaag4dOmR1abhARUVFZs+ePWbPnj1GknnqqafMnj173EsZzps3zzgcDvPGG2+YvXv3mrvvvvusyx9dccUVZsOGDea9994zvXr1OuvyRx07djQ7duwwO3bsMHFxcSx/ZLH777/fOBwOs3nzZnPkyBH3z4kTJ9xj6H/9NW3aNLN161Zz8OBB88EHH5jp06ebBg0amIyMDGMMvfdHp6/aYQzXgC8gSNdjf/nLX0zLli1NUFCQue6669xLZqFueeutt4ykKj8jRowwxriWQJo5c6aJiooywcHB5uabbzZ79+71eI7i4mIzfvx4ExERYUJCQszAgQNNTk6Ox5gffvjBDB8+3ISFhZmwsDAzfPhwk5+fX1tvE2dxtr5LMosXL3aPof/11z333OP+b3izZs1M79693SHaGHrvj84M0lwD1rMZY4w198IBAACAuos50gAAAIAXCNIAAACAFwjSAAAAgBcI0gAAAIAXCNIAAACAFwjSAAAAgBcI0gAAAIAXCNIAgHpnyZIl+tnPfmZ1GQDqOYI0APiIkSNHymazyWazKTAwUK1bt9aUKVN0/Phxq0uzxMWcj2HDhumTTz6phSoB+LMAqwsAAFTq37+/Fi9eLKfTqW3btmnUqFE6fvy4Fi1aVGWs0+lUYGCgBVWe26Wu6ULOx+lCQkIUEhJyyeoAgLPhjjQA+JDg4GBFRUUpJiZGSUlJGj58uFauXClJSklJ0S9+8Qv9/e9/V+vWrRUcHCxjjAoKCnTfffepefPmatKkiXr16qX333/f/Zzvv/++evbsqbCwMDVp0kTx8fHavXu3JOnw4cMaNGiQwsPDFRoaqmuvvVZr166VdPbpEStXrpTNZnNve1vTpTgf1b2vM2v//PPPddtttykyMlKNGzfW9ddfrw0bNlxwPQBwOu5IA4APCwkJkdPpdG9/9tlnev3117V8+XLZ7XZJ0oABAxQREaG1a9fK4XDohRdeUO/evfXJJ58oIiJCw4cPV+fOnbVo0SLZ7XZlZ2e77xqPGzdOpaWl2rp1q0JDQ7V//341btz4gmr0pqZLcT6qe19nOnbsmH71q19p9uzZatiwoV5++WUNGjRIBw4cUIsWLbyuB4B/I0gDgI/atWuXli1bpt69e7v3lZaW6pVXXlGzZs0kSZs2bdLevXuVl5en4OBgSdKCBQu0cuVK/etf/9J9992nnJwcPfTQQ7r66qslSW3btnU/X05OjoYMGaK4uDhJUuvWrS+4Tm9q8saZ56O693WmTp06qVOnTu7t2bNna8WKFVq1apXGjx/vVT0AQJAGAB+yevVqNW7cWKdOnZLT6dRtt92mhQsXuo+3bNnSHVglKSsrS8eOHdNll13m8TzFxcX6/PPPJUmTJ0/WqFGj9Morr6hPnz668847ddVVV0mSJk6cqPvvv18ZGRnq06ePhgwZoo4dO15Qzd7UdL6qOx/Vva8zHT9+XKmpqVq9erW++eYbnTp1SsXFxcrJybmgegDgdARpAPAhPXv21KJFixQYGKjo6OgqUxVCQ0M9tsvLy3X55Zdr8+bNVZ6rYo5wSkqKkpKStGbNGr355puaOXOm0tPTdccdd2jUqFHq16+f1qxZo4yMDKWlpenJJ5/UhAkT1KBBAxljPJ7z9GkmF1PT+arufFT3vs700EMPaf369VqwYIHatGmjkJAQ/eY3v1FpaekF1QMApyNIA4APCQ0NVZs2bc57/HXXXafc3FwFBAToyiuvPOe4du3aqV27dvrDH/6gu+++W4sXL3YHzpiYGI0ZM0ZjxozRtGnT9OKLL2rChAlq1qyZioqKdPz4cXdYzs7OvmQ1nY+fOh/Vva/Tbdu2TSNHjnQfO3bsmA4dOnRRtQEAq3YAQB3Wp08fJSQk6Pbbb9f69et16NAhbd++XX/84x+1e/duFRcXa/z48dq8ebMOHz6sf//733r33XfVoUMHSdKkSZO0fv16HTx4UO+99542bdrkPta1a1c1atRI06dP12effaZly5ZpyZIlF13TpfBT7+tMbdq00RtvvKHs7Gy9//77SkpKUnl5+SWpBYD/IkgDQB1ms9m0du1a3XzzzbrnnnvUrl073XXXXTp06JAiIyNlt9v1ww8/6He/+53atWunoUOH6tZbb1VqaqokqaysTOPGjVOHDh3Uv39/tW/fXs8995wkKSIiQkuXLtXatWsVFxenf/7zn0pJSbnomiTp0KFDstlsZ53+cT5+6n2d6emnn1Z4eLi6d++uQYMGqV+/frruuuu8em0AqGAzZ06AAwCghm3evFl33HGHvvjiC4WHh1tdDgB4hTvSAIBat27dOk2fPp0QDaBO4440AAAA4AXuSAMAAABeIEgDAAAAXiBIAwAAAF4gSAMAAABeIEgDAAAAXiBIAwAAAF4gSAMAAABeIEgDAAAAXiBIAwAAAF4gSAO334XOAAAACklEQVQAAABe+H/6fhqXuJruVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.plot(df['P(psi)'] , df['2P/muZ'] , marker = 'x',c='red',label='2P/muZ vs P')\n",
    "\n",
    "plt.xlabel('Pressure, Psia')\n",
    "plt.ylabel('2P/muZ')\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P(psi)</th>\n",
       "      <th>mu_g</th>\n",
       "      <th>z</th>\n",
       "      <th>2P/muZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>0.01286</td>\n",
       "      <td>0.937</td>\n",
       "      <td>66391.033227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>800</td>\n",
       "      <td>0.01390</td>\n",
       "      <td>0.882</td>\n",
       "      <td>130507.838627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200</td>\n",
       "      <td>0.01530</td>\n",
       "      <td>0.832</td>\n",
       "      <td>188536.953243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600</td>\n",
       "      <td>0.01680</td>\n",
       "      <td>0.794</td>\n",
       "      <td>239894.446444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.01840</td>\n",
       "      <td>0.770</td>\n",
       "      <td>282326.369283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2400</td>\n",
       "      <td>0.02010</td>\n",
       "      <td>0.763</td>\n",
       "      <td>312982.922869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2800</td>\n",
       "      <td>0.02170</td>\n",
       "      <td>0.775</td>\n",
       "      <td>332986.472425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3200</td>\n",
       "      <td>0.02340</td>\n",
       "      <td>0.797</td>\n",
       "      <td>343167.218951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3600</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>0.827</td>\n",
       "      <td>348246.674728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4000</td>\n",
       "      <td>0.02660</td>\n",
       "      <td>0.860</td>\n",
       "      <td>349711.488022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4400</td>\n",
       "      <td>0.02831</td>\n",
       "      <td>0.896</td>\n",
       "      <td>346924.357875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    P(psi)     mu_g      z         2P/muZ\n",
       "0        0  0.01270  1.000       0.000000\n",
       "1      400  0.01286  0.937   66391.033227\n",
       "2      800  0.01390  0.882  130507.838627\n",
       "3     1200  0.01530  0.832  188536.953243\n",
       "4     1600  0.01680  0.794  239894.446444\n",
       "5     2000  0.01840  0.770  282326.369283\n",
       "6     2400  0.02010  0.763  312982.922869\n",
       "7     2800  0.02170  0.775  332986.472425\n",
       "8     3200  0.02340  0.797  343167.218951\n",
       "9     3600  0.02500  0.827  348246.674728\n",
       "10    4000  0.02660  0.860  349711.488022\n",
       "11    4400  0.02831  0.896  346924.357875"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[:,:4]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-73-27b47a4d8b2d>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['A_i'][j] = (df['2P/muZ'][j] + df['2P/muZ'][j-1])*400/2\n"
     ]
    }
   ],
   "source": [
    "df['A_i'] = np.zeros((12,1))\n",
    "\n",
    "\n",
    "\n",
    "for j in range(1, 12):\n",
    "    \n",
    "    df['A_i'][j] = (df['2P/muZ'][j] + df['2P/muZ'][j-1])*400/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['m(P)'] = np.cumsum(df['A_i'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P(psi)</th>\n",
       "      <th>mu_g</th>\n",
       "      <th>z</th>\n",
       "      <th>2P/muZ</th>\n",
       "      <th>A_i</th>\n",
       "      <th>m(P)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>0.01286</td>\n",
       "      <td>0.937</td>\n",
       "      <td>66391.033227</td>\n",
       "      <td>1.327821e+07</td>\n",
       "      <td>1.327821e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>800</td>\n",
       "      <td>0.01390</td>\n",
       "      <td>0.882</td>\n",
       "      <td>130507.838627</td>\n",
       "      <td>3.937977e+07</td>\n",
       "      <td>5.265798e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200</td>\n",
       "      <td>0.01530</td>\n",
       "      <td>0.832</td>\n",
       "      <td>188536.953243</td>\n",
       "      <td>6.380896e+07</td>\n",
       "      <td>1.164669e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600</td>\n",
       "      <td>0.01680</td>\n",
       "      <td>0.794</td>\n",
       "      <td>239894.446444</td>\n",
       "      <td>8.568628e+07</td>\n",
       "      <td>2.021532e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.01840</td>\n",
       "      <td>0.770</td>\n",
       "      <td>282326.369283</td>\n",
       "      <td>1.044442e+08</td>\n",
       "      <td>3.065974e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2400</td>\n",
       "      <td>0.02010</td>\n",
       "      <td>0.763</td>\n",
       "      <td>312982.922869</td>\n",
       "      <td>1.190619e+08</td>\n",
       "      <td>4.256592e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2800</td>\n",
       "      <td>0.02170</td>\n",
       "      <td>0.775</td>\n",
       "      <td>332986.472425</td>\n",
       "      <td>1.291939e+08</td>\n",
       "      <td>5.548531e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3200</td>\n",
       "      <td>0.02340</td>\n",
       "      <td>0.797</td>\n",
       "      <td>343167.218951</td>\n",
       "      <td>1.352307e+08</td>\n",
       "      <td>6.900839e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3600</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>0.827</td>\n",
       "      <td>348246.674728</td>\n",
       "      <td>1.382828e+08</td>\n",
       "      <td>8.283666e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4000</td>\n",
       "      <td>0.02660</td>\n",
       "      <td>0.860</td>\n",
       "      <td>349711.488022</td>\n",
       "      <td>1.395916e+08</td>\n",
       "      <td>9.679583e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4400</td>\n",
       "      <td>0.02831</td>\n",
       "      <td>0.896</td>\n",
       "      <td>346924.357875</td>\n",
       "      <td>1.393272e+08</td>\n",
       "      <td>1.107285e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    P(psi)     mu_g      z         2P/muZ           A_i          m(P)\n",
       "0        0  0.01270  1.000       0.000000  0.000000e+00  0.000000e+00\n",
       "1      400  0.01286  0.937   66391.033227  1.327821e+07  1.327821e+07\n",
       "2      800  0.01390  0.882  130507.838627  3.937977e+07  5.265798e+07\n",
       "3     1200  0.01530  0.832  188536.953243  6.380896e+07  1.164669e+08\n",
       "4     1600  0.01680  0.794  239894.446444  8.568628e+07  2.021532e+08\n",
       "5     2000  0.01840  0.770  282326.369283  1.044442e+08  3.065974e+08\n",
       "6     2400  0.02010  0.763  312982.922869  1.190619e+08  4.256592e+08\n",
       "7     2800  0.02170  0.775  332986.472425  1.291939e+08  5.548531e+08\n",
       "8     3200  0.02340  0.797  343167.218951  1.352307e+08  6.900839e+08\n",
       "9     3600  0.02500  0.827  348246.674728  1.382828e+08  8.283666e+08\n",
       "10    4000  0.02660  0.860  349711.488022  1.395916e+08  9.679583e+08\n",
       "11    4400  0.02831  0.896  346924.357875  1.393272e+08  1.107285e+09"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.style.use('default')\n",
    "# plt.figure(figsize=(8,4))\n",
    "\n",
    "# plt.plot(df['P(psi)'] , df['2P/muZ'] , marker = 'x',c='red',label='2P/muZ vs P')\n",
    "\n",
    "# plt.xlabel('Pressure, Psia')\n",
    "# plt.ylabel('2P/muZ')\n",
    "\n",
    "# plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "a1 = fig.add_axes([0,0,1,1])\n",
    "\n",
    "x = df['P(psi)']\n",
    "a1.plot(x,df['2P/muZ'])\n",
    "a1.set_ylabel('2P/muZ')\n",
    "\n",
    "a2 = a1.twinx()\n",
    "a2.plot(x, df['m(P)'],'ro-')\n",
    "a2.set_ylabel('m(P)')\n",
    "\n",
    "fig.legend(labels = ('2P/muZ','m(P)'))\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.plot(df['P(psi)'] , df['2P/muZ'] , marker = 'x',c='red',label='2P/muZ vs P')\n",
    "plt.plot(df['P(psi)'] , df['m(P)']/10000 , marker = '*',label='m(P) (x10000 psi^2/cP) vs P')\n",
    "\n",
    "plt.xlabel('Pressure, Psia')\n",
    "plt.ylabel('2P/muZ')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Real Gas (Radial) Flow')\n",
    "\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now All we need to do is find the corresponding m(P) at Pe and Pwf and solve for the Flow Rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. m(Pwf)\n",
    "\n",
    "m_Pwf = float(df[df['P(psi)'] == 3600]['m(P)'])\n",
    "\n",
    "m_Pe =  float(df[df['P(psi)'] == 4400]['m(P)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "Qg = k*h*(m_Pe - m_Pwf)/(1422*T*np.log(re/rw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39293.270285254715"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gas Flow rate = 39293.270285254715 MSCF/Day.\n"
     ]
    }
   ],
   "source": [
    "print(f'Gas Flow rate = {Qg} MSCF/Day.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
